Inside Unmanned Systems

APR-MAY 2018

Inside Unmanned Systems provides actionable business intelligence to decision-makers and influencers operating within the global UAS community. Features include analysis of key technologies, policy/regulatory developments and new product design.

Contents of this Issue

Navigation

Page 58 of 67

59 April/May 2018 unmanned systems
inside
ENGINEERING. PRACTICE. POLICY.
gin, the drones can easily crash. This suggests
map-based approaches may prove impracti-
cal in real-world settings when the exact posi-
tion of every obstruction is unpredictable or
unknown.
"There have been many challenges along the
way for researchers trying to push the limits
of autonomous navigation in unknown envi-
ronments," NanoMap lead researcher Peter
Florence said. "Things are just really hard
when you are in real, unknown, diverse envi-
ronments, like out in a forest you've never seen
before."
NanoMap, on the other hand, considers a
drone's position to be uncertain, and models
and accounts for that uncertainty. This ap-
proach can help drones more reliably f ly at
higher speeds in close quarters, Florence said.
Mapping techniques for drones often rely on
so-called "occupancy grids," in which the many
measurements that drones take are incorpo-
rated into a 3-D representation of the world.
However, such measurements can prove both
unreliable and difficult to gather quickly, lim-
iting high-speed maneuvering in cluttered
spaces.
Instead of operating under the assumption
that avoiding obstacles requires taking many
different measurements and figuring out each
object's exact location in space, NanoMap
aims to gather enough information to know
the general area of an object. It also searches
its memory of what it has seen previously to
anticipate how it might best move to places it
currently does not see.
The MIT researchers tested NanoMap
as part of the Defense Advanced Research
Projects Agency's Fast Lightweight Autonomy
program. Their experiments involved a DJI
Flame Wheel quad-rotor drone, a dual-core
Intel NUC i7 computer processor, an Intel
RealSense R200 depth camera sensor for
outdoor environments, an ASUS Xtion depth
camera sensor for indoor environments, and
a Point Grey Flea3 camera and ADIS 16448
inertial measurement unit to help the drone
keep track of its position and orientation.
Using NanoMap, the drone could f ly at
speeds of up to 10 meters per second in for-
ested canopy environments and 8 meters per
second in indoor warehouse environments.
"Working on robots racing through forests is
just intrinsically super fun to me," Florence
said.
These experiments revealed
how much accounting for uncer-
tainty helped the drone f ly. For
example, if NanoMap did not
model for uncertainty and the
drone drifted just 5 percent away
from where it was expected to be, it would
crash 28 percent of f lights. Meanwhile, when
the drone accounted for uncertainty, the crash
rate dropped to 2 percent of these f lights.
"The results of MIT's NanoMap are clearly
impressive. Flying at that speed is definitely a
challenge from the algorithmic and hardware
point of view," said Antonio Loquercio, an ar-
tificial intelligence researcher at the University
of Zurich in Switzerland, who did not take part
in the research on NanoMap.
Drone speed could also increase given
depth sensors with greater range and drones
with better acceleration and deceleration ca-
pabilities. Future versions of NanoMap may
BY THE
NUMBERS
Up to 10 Meters/Second
The speed a NanoMap-
equipped drone could fl y
in a forest
Up to 8 Meters/Second
The speed a NanoMap-
equipped drone could fl y
in a warehouse
Photo courtesy Jonathan How, MIT
" THINGS ARE JUST REALLY HARD WHEN
YOU ARE IN REAL, UNKNOWN, DIVERSE
ENVIRONMENTS, LIKE OUT IN A FOREST
YOU'VE NEVER SEEN BEFORE."
Peter Florence, lead researcher, NanoMap